Extracting Bounded-level Modules from Deductive RDF Triplestores

Marie-Christine Rousset 1, 2, 3 Federico Ulliana 4
4 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We present a novel semantics for extracting bounded-level modules from RDF ontologies and databases augmented with safe inference rules, a la Datalog. Dealing with a recursive rule language poses challenging issues for defining the module semantics, and also makes module extraction algorithmically unsolvable in some cases. Our results include a set of module extraction algorithms compliant with the novel semantics. Experimental results show that the resulting framework is effective in extracting expressive modules from RDF datasets with formal guarantees, whilst controlling their succinctness.
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Communication dans un congrès
AAAI: Conference on Artificial Intelligence, Jan 2015, Austin, Texas, United States. AAAI'15: 29th Conference on Artificial Intelligence
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01086951
Contributeur : Federico Ulliana <>
Soumis le : jeudi 15 janvier 2015 - 10:02:37
Dernière modification le : samedi 10 juin 2017 - 01:08:16
Document(s) archivé(s) le : vendredi 11 septembre 2015 - 06:49:06

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Marie-Christine Rousset, Federico Ulliana. Extracting Bounded-level Modules from Deductive RDF Triplestores. AAAI: Conference on Artificial Intelligence, Jan 2015, Austin, Texas, United States. AAAI'15: 29th Conference on Artificial Intelligence. <lirmm-01086951>

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